Future of Precision Medicine with AI
Abstract
Precision medicine — the “tailoring of medical treatment to the individual characteristics, needs and preferences of a patient” that has led to personalized new approaches to treatment based on genetics, environmental and lifestyle factors — is a promising but fast-moving paradigm for delivery of health care. [Relevant/Material Odyssey Markers Related] Content This module advocates an approach that considers visible and invisible factors for personalised approach in drug therapy. But the complexity and size of the data needed for precision medicine is a serious challenge. In this regard, we find that the Artificial Intelligence (AI) such as machine learning and deep learning techniques are becoming a major enabler and creating a positive environment for future. AI tools can analyse medical images, lab results and patient histories for patterns that a trained clinician might miss with astonishing accuracy. In genomics, artificial intelligence expedites the analysis of DNA sequences and the discovery of gene-disease relationships essential for personalized therapeutics. Predictive analytics helps to predict disease risk, treatment response, while Natural Language Processing (NLP) tools further aid precision medicine extracting insights from unstructured clinical texts. Challenges in Integrating AI with Precision Medicine Although AI has the potential to revolutionize precision medicine, its integration is not without challenges. To enable this effective, ethical and equitable implementation of AI, we need to overcome challenges surrounding data privacy, algorithmic bias and access inequities to AI technologies. Additionally, regulatory and clinical approval pose major challenges to converting AI models into usable clinical applications. AI is also set to have a transformative impact on how we approach precision medicine, enhancing clinical decision-making, accelerating drug discovery, and harnessing healthcare accessibility in the future. By continuing with interdisciplinary.
